B2C环境下闭环供应链定价模型的优化

发布时间：2019-01-03 20:49

【摘要】：随着互联网渗透生活,人们消费水平的提高和需求的不断变化,各类产品的市场生命周期不断缩短,使废旧产品数量也在不断增加,而人们环保意识的逐渐增强,企业受到经济利益的驱动,发展并优化供应链逐渐变成企业建立竞争优势的有效手段。闭环供应链管理最重要的一个职能是协调定价,它与产品的回收、需求和系统运作效率有一定的关系。然而传统的博弈理论只能系统地分析其供应链某个因素的变化对其利润的影响,而不能帮企业做出定价的最终决策,所以我们使用遗传算法,对供应链模型进行仿真分析。遗传算法是一种根据生物界自然选择和自然遗传机制的随机搜索算法。它特别适用解决使用传统搜索方法难以处理的复杂问题和非线性问题,可广泛用于机器学习、组合优化、人工智能等领域,是21世纪智能计算中的关键技术之一。鉴于此,本文在总结相关文献研究的基础之上,对B2C环境下闭环供应链定价决策建立模型,并使用遗传算法进行优化决策。其主要研究内容如下:(1)对闭环供应链产品定价问题进行建模,建立了同一市场的不同决策主体下的决策模型:分散化决策模型、集中化决策模型和基于分散化决策下的契约协调成本分担契约决策模型;(2)提出了基于多目标遗传算法的闭环供应链产品定价求解方案,应用多目标遗传算法对闭环供应链产品进行优化定价;(3)对决策模型及优化定价方案进行仿真分析,快捷准确地知道如何确定销售价格和回收价格可以使制造商和零售商利润达到最大,为以后企业的定价管理决策提供了很大的帮助。本文的研究结果表明,遗传算法对企业的定价决策有很直观的辅助作用;B2C环境下的闭环供应链项目选择合理契约协调机制可以使得闭环供应链系统获得最佳的经济收益。
[Abstract]:With the penetration of the Internet into life, the improvement of people's consumption level and the constant change of demand, the market life cycle of all kinds of products is continuously shortened, the quantity of waste products is also increasing, and people's awareness of environmental protection is gradually strengthened. Driven by economic benefits, the development and optimization of supply chain has gradually become an effective means for enterprises to establish competitive advantage. One of the most important functions of closed-loop supply chain management is to coordinate pricing, which has a certain relationship with product recovery, demand and system operation efficiency. However, the traditional game theory can only systematically analyze the influence of a certain factor in its supply chain on its profit, but can not help the enterprise to make the final decision of pricing. So we use genetic algorithm to simulate the supply chain model. Genetic algorithm is a random search algorithm based on natural selection and natural genetic mechanism. It is especially suitable for solving complex and nonlinear problems which are difficult to deal with by using traditional search methods. It can be widely used in the fields of machine learning, combinatorial optimization, artificial intelligence and so on. It is one of the key technologies in intelligent computing in the 21st century. In view of this, on the basis of summarizing the relevant literatures, this paper establishes the model of the closed-loop supply chain pricing decision in B2C environment, and uses genetic algorithm to optimize the decision. The main research contents are as follows: (1) the pricing problem of closed-loop supply chain products is modeled, and the decision model of different decision makers in the same market is established: decentralized decision model. Centralized decision model and contract coordination cost sharing contract decision model based on decentralized decision; (2) A solution of closed-loop supply chain product pricing based on multi-objective genetic algorithm is proposed, and the multi-objective genetic algorithm is applied to optimize the pricing of closed-loop supply chain products. (3) the decision model and the optimized pricing scheme are simulated and analyzed, and how to determine the sales price and the recovery price quickly and accurately can make the manufacturer and retailer profit maximum. It provides a great help for the decision of pricing management in the future. The results of this paper show that the genetic algorithm can directly assist the enterprise pricing decision, and the reasonable contract coordination mechanism of the closed-loop supply chain project selection under B2C environment can make the closed-loop supply chain system obtain the best economic benefits.
【学位授予单位】：湖南科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F274

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